ECG Data Compression using DWT
Anubhuti Khare1, Manish Saxena2, Vijay B. Nerkar3
1Dr. Anubhuti Khare, Reader, Department of Electronics and Communication, University Institute of Technology, Rajeev Gandhi Technical University, Bhopal, (M.P.), India.
2Manish Saxena, Head of Electronics and Communication Department, Bansal Institute of Science and Technology Bhopal (M.P.), India.
3Vijay B. Nerkar , Mtech (Digital Communication), Bansal Institute of Science and Technology Bhopal (M.P.), India.
Manuscript received on October 06, 2021. | Revised Manuscript received on October 12, 2021. | Manuscript published on October 30, 2011 . | PP: 11-13 | Volume-1 Issue-1, October 2021. | Retrieval Number: A0097101111/2011©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: though digital storage media is not expensive and computational power has exponentially increased in past few years, the possibility of electrocardiogram (ECG) compression still attracts the attention, due to the huge amount of data that has to be stored and transmitted; the amount that grows (depending upon the sampling rate, quantization levels and number of sensors) at the rate of 7.5-540 KB per minute per patient, depending upon the time and amplitude, sampling rate and number of sensors. Besides the increased storage capacity for archival purposes, ECG compression allows real-time transmission over telephone networks, economic off-line transmission to remote interpretation sites, improves Holter monitor systems and enables efficient ECG rhythm analysis algorithms. A wide range of compression techniques based on different transformation techniques like DCT, FFT; DST & DCT2 were evaluated to find an optimal compression strategy for ECG data compression. Wavelet compression techniques were found to be optimal in terms of compression.
Keywords: ECG, Compression, DCT, DWT, CR and PRD